docstr: Set constant gradient clipping during the training process.
  In order to take effect, it needs to be called before fit.
  
  :param min: The minimum value to clip by.
  :param max: The maximum value to clip by.
  :return:
docstr_tok: ["Set","constant","gradient","clipping","during","the","training","process",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".",":","param","min",":","The","minimum","value","to","clip","by",".",":","param","max",":","The","maximum","value","to","clip","by",".",":","return",":"]
code: def set_constant_gradient_clipping(self, min, max):
      """
      Set constant gradient clipping during the training process.
      In order to take effect, it needs to be called before fit.
  
      :param min: The minimum value to clip by.
      :param max: The maximum value to clip by.
      :return:
      """
      self.nn_estimator.setConstantGradientClipping(min, max)
      self.estimator.set_constant_gradient_clipping(min, max)
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 10 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.set_constant_gradient_clipping(min, max) without return types